Deike Albers

ORCID: 0000-0002-9923-0724
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About
Contact & Profiles
Research Areas
  • Traffic and Road Safety
  • Human-Automation Interaction and Safety
  • Safety Warnings and Signage
  • Injury Epidemiology and Prevention
  • Urban Transport and Accessibility
  • Traffic control and management
  • Ergonomics and Human Factors
  • Transportation and Mobility Innovations
  • Transportation Planning and Optimization
  • Behavioral Health and Interventions
  • Cardiac Arrest and Resuscitation
  • Persona Design and Applications
  • Autonomous Vehicle Technology and Safety
  • Urban Green Space and Health
  • Traffic Prediction and Management Techniques
  • Agriculture and Farm Safety
  • Resilience and Mental Health
  • IoT and GPS-based Vehicle Safety Systems

Technical University of Munich
2018-2022

Chalmers University of Technology
2019

10.1016/j.trf.2020.03.004 article EN Transportation Research Part F Traffic Psychology and Behaviour 2020-03-18

Automated detection of motorcycle helmet use through video surveillance can facilitate efficient education and enforcement campaigns that increase road safety. However, existing approaches have a number shortcomings, such as the inabilities to track individual motorcycles multiple frames, or distinguish drivers from passengers in use. Furthermore, datasets used develop are limited terms traffic environments density variations. In this paper, we propose CNN-based multi-task learning (MTL)...

10.1109/access.2020.3021357 article EN cc-by IEEE Access 2020-01-01

This paper aims to describe and test novel computational driver models, predicting drivers' brake reaction times (BRTs) different levels of lead vehicle braking, during driving with cruise control (CC) silent failures adaptive (ACC).Validated models BRTs automation are lacking but important for assessing the safety benefits automated driving.Two alternative response ACC proposed: a looming prediction model, assuming that drivers embody generative model ACC, lower gain arousal decreases due...

10.1177/0018720819875347 article EN Human Factors The Journal of the Human Factors and Ergonomics Society 2019-10-07

The projected introduction of conditional automated driving systems to the market has sparked multifaceted research on human–machine interfaces (HMIs) for such systems. By moderating roles human driver and automation system, HMI is indispensable in avoiding side effects as mode confusion, misuse, disuse. In addition safety aspects, usability HMIs plays a vital role improving trust acceptance system. This paper aggregates common methods findings based an extensive literature review. Empirical...

10.3390/info11050240 article EN cc-by Information 2020-04-28

Facilitating safe pedestrian road crossings is a major prerequisite for urban environments. In multiple cities around the world, 3D crosswalks have been painted, which provoke an optical illusion, of e.g., crosswalk floating above road, in car drivers who approach crosswalk. However, to date, no detailed study users' safety related perceptions on has conducted. Hence, we investigated drivers' and pedestrians' crosswalk, how they rate its comparison traditional (non-3D illusion) crosswalks....

10.1016/j.trf.2022.10.003 article EN cc-by Transportation Research Part F Traffic Psychology and Behaviour 2022-10-18

Although motorcycle helmets are vital to prevent heavy injuries and fatalities in crashes, only one third of low- middle-income countries (LMIC) regularly collects helmet use data. When data is available, it often undetailed or based on small sample sizes. Hence, methods for regular detailed monitoring LMIC needed. In the light application LMIC, resource-efficiency these has be considered. Common estimate (naturalistic observation, self-reports questionnaire surveys, hospital surveys) were...

10.1136/injuryprevention-2018-safety.500 article EN Abstracts 2018-09-20

Conditionally automated driving (L3) implies repeated transitions of the responsibility between human operator and system. This research examines users’ attitudes towards speech outputs as potential features for human-machine interfaces L3 driving. The Kano method is applied to identify scenarios where users prefer outputs. After a test drive with an vehicle, N = 42 drivers take part in survey on different scenarios. results preferences critical situations. In non-critical application areas,...

10.54941/ahfe1002484 article EN AHFE international 2022-01-01

Human behavior plays a major role in the causation of road traffic crashes and severity their outcomes. The accurate detailed registration user is therefore vital to identify variables that can decrease number crashes. Naturalistic observation field presents main pillar injury prevention research. Since highly time-consuming when carried out by human observers, video-based naturalistic has been proposed as an efficient alternative. With this poster we want present attendees with low-cost...

10.1136/injuryprevention-2018-safety.222 article EN Abstracts 2018-09-20

The usability of human-machine-interfaces (HMIs) for automated driving systems (ADS) gains importance with the imminent introduction SAE L3 vehicles [15]. Assuming global proliferation vehicles, a common understanding ADS HMIs and its application in research industry is indispensable. In reference to ISO 9241-11 [8], this virtual workshop aims identify potential differences resulting assessment usability. international audience Automotive-UI poses an ideal setting purpose by bringing...

10.1145/3409251.3411737 article EN 2020-09-19
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